DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 03/12/2026.
Claim 28 has been amended and is hereby entered.
Claims 21-27 and 35-41 have been canceled.
Claims 42-48 have been added.
Claims 28-34 and 42-48 are currently pending and have been examined.
This action is made FINAL.
Response to Arguments
Examiner notes that cancelation of claims 21-27 and 35-41 render the previous restriction requirement moot. A restriction requirement is not appropriate for newly introduced claims 42-48. Therefore, although not particularly argued by Applicant, the previous restriction requirement between claims 21 and 28 has been withdrawn.
Applicant’s arguments, see pages 7-8, filed 03/12/2026, with respect to the 35 U.S.C. 112(a) rejections of claims 28-34 have been fully considered and are persuasive. The 35 U.S.C. 112(a) rejections of claims 28-34 have been withdrawn.
Specifically, Applicant has amended the removal distance limitations at issue in claim 28 to now read “determining the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle” and “outputting, by the signaling device, a load space position signal indicating the location for placing each object and orientation information for placing the object at its respective location.” As Applicant argues on pages 7-8, these amended limitations have sufficient written description support in the as-filed specification in at least paragraphs [0016]-[0017], [0038], and [0042]. New independent claim 42 similarly reflects the amended claim language of claim 28. Accordingly, the 35 U.S.C. 112(a) written description rejections of claims 28-34 have been withdrawn.
Applicant’s arguments, see pages 8-14, filed 03/12/2026, with respect to the 35 U.S.C. 101 rejections of claims 28-34 have been fully considered but are not persuasive. The 35 U.S.C. 101 rejections of claims 28-34 have been maintained. Newly added claims 42-48 are rejected under 35 U.S.C. 101 for similar reasoning.
After summarizing the rejections on page 8 of Remarks, Applicant argues on page 9 that the claims do not recite a mental process. Applicant argues that the amended limitations reciting a camera system and the use of sensor data from the camera system to determine the presence of a person placing objects in the vehicle and identification information of the objects preclude the claim from reciting a mental process because a human mind cannot acquire sensor data from a camera system or process such sensor data to perform the determining steps. Examiner respectfully disagrees. MPEP 2106.04(a)(2) III.C. recites “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process”. In the instant claims, the claimed invention is at least performing a mental process in a computer environment and using a computer as a tool to perform the mental process. Particularly, a human can view and recognize a person entering a vehicle and placing objects in the vehicle and likewise observe properties of a package being loaded and compare such properties with information of a route plan to determine identifying information for the package. The claimed invention is performing this mental process in a computing environment and using the computer as a tool to recognize a person loading packages and identifying the packages being loaded. While not explicitly argued here, Examiner notes that other limitations of the claimed invention such as “determining that a first number of objects present in the vehicle matches a second number of objects in the route plan; determining, based on the route plan and the identification information for each object, a location for placing each object within the vehicle; determining the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle” also recite mental processes (i.e. a human can count packages in a vehicle to determine whether the count matches the number of packages in a route plan, determine the location based on the route plan and identification information as Applicant indicates human delivery drivers do in [0003]-[0004] of the specification, and determining such a location such that a removal distance is left between the packages to allow for later retrieval). Applicant’s arguments that the claimed invention does not recite a Mental Process are not persuasive.
Applicant next argues across pages 9-11 that the claims integrate abstract ideas into a practical application by reciting an improvement to technology. Applicant argues that the mental burden on delivery drivers and the delivery inefficiencies resulting from each driver’s particular method for and experience with loading packages onto a delivery vehicle are resolved by the claimed invention on page 10. Applicant argues that [0017] and [0047] of the specification reciting the eased burden for the user and the enhanced delivery efficiency provided by the object placement logic are the alleged technical improvements. Applicant argues that the claims reflect the alleged technical improvement on pages 10-11, with the camera system replacing the “mental map” and the computer vision technology automates “what was previously a subjective mental process”. Finally, Applicant argues that the load space position signal provides “real-time guidance based on objective criteria” as part of the technical improvement. The removal distance consideration is also argued as a technical improvement by Applicant. Examiner respectfully disagrees.
First, Examiner notes that the use of a removal distance consideration falls into the abstract idea. A human can judge where to place objects in a vehicle with enough space between them such that the human will be able to remove the objects later. Accordingly, the removal distance consideration is part of the abstract idea and not a technical improvement. Second, the alleviation of the mental burden on delivery drivers is not an improvement in technology, as an experienced supervisor/manager instructing a deriver on the optimal location to place each package (i.e. performing the abstract idea/mental process of the claimed invention) would still result in the mental burden of the delivery driver being offloaded onto another party. While the driver may not have to worry about making placement decisions in the claimed system, that would also be true if another human were performing the steps as well. Therefore, the reduction in mental burden on the driver is not an improvement to technology. Furthermore, the use of a camera system to replace a “mental map” and “computer vision technology” the replace “a subjective mental process” is merely applying generic computing components to the abstract idea of the claimed invention (in Applicant’s words, the “mental map” of the load space and the “subjective mental process” of determining the identification of the packages being loaded). Particularly, Applicant’s claimed invention uses these (in combination with the other additional elements listed in the rejection below) as tools to perform the abstract ideas of the claims Per MPEP 2106.05(f)(1) and (2), “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” and “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Applicant’s claimed invention and specification do not provide any technical improvements to the camera system or the computer vision technology itself. Instead, Applicant’s claims recite the intended result of determining the presence of a human, the identification information of a package, etc. using the data from the camera system and object identification logic having an algorithm “with” machine learning. The claims do not recite a particular way of achieving the results using the additional elements, and the additional elements are being used as tools to perform the abstract steps. Accordingly, Applicant’s arguments that the specification and claims recite improvements to technology and are therefore eligible at Step 2A prong Two are not persuasive.
Applicant next argues on Page 11 that the claims are analogous to eligible claims in Examples 36 and 45. Regarding Example 36, Applicant argues that the claimed camera system is analogous to the “high-resolution video camera array” and provides a similar technical improvement to the technical problem of automatically tracking objects and their positions using vision systems. Examiner respectfully disagrees. Regarding claim 36, Examiner notes that technical improvement was the tracking of objects across multiple camera views to determine their 3D location, which was explicitly considered in the invention. The eligible claims reflected this improvement with the camera array with overlapping views and the reconstruction of 3D coordinates using the multiple overlapping images. In contrast, the claimed invention recites using “sensor data” from the cameras to recognize people and identify objects without the requirements of a particular camera arrangement or particular technique recognized as an improvement in the specification. Examiner notes that while camera systems are recited in [0029] and [0093]-[0095], there is no indication that the camera system is solving a technical problem with object tracking itself (like the multi-camera problem of Example 36). Instead, specification [0093] and [0100] recite that the cameras are adapted to acquire various objects and properties without reciting any particular problems with camera systems. As discussed above, the “sensor data” is recited in the claims as merely a tool to determine the information recited in the claims. Unlike Example 36 in which a particular arrangement and functionality was claimed that solved an indicated problem with the technology of object tracking across multiple camera views, the claimed invention is using its camera system as a tool without reciting technical improvements in the specification or claims. Therefore, Example 36 recites an improvement to the technology of object tracking while the claimed invention does not recite a technical improvement. Applicant’s arguments that the claimed invention is analogous to Example 36 are not persuasive.
Applicant argues regarding Example 45 that the signaling device outputting a load space position signal is controlling how objects are physically placed in the vehicle and is therefore analogous to the control of the injection molding apparatus of Example 45. Examiner respectfully disagrees. Specifically, Examiner notes that the claims of Example 45 that control the injection molding directly control the molding using control signals to physically open/eject the mold and adjust the temperature of the mold. In contrast, the signaling device outputting a signal is not physically placing the packages is the determined location. Instead, the output signal serves as an instruction for a human user to place the package in the designated spot (see specification [0097], [0102] “a respective load space position signal is outputted by a signaling device 52. The load space position signal indicates the respective determined load space position 48 for the respective object 18. As a result, the user is assisted in loading the load space 14 with the objects 18, because the user no longer has to locate the load space positions 48 himself”). Therefore, the output signal of the claimed invention only controls the placement of the object as far as a human user follows its instructions. Providing instructions to a human falls under the certain methods of organizing human activity subcategory of managing personal behavior, whereas the control of Example 45 directly operates the injection mold instead of merely instructing a user to open the mold, etc.
Applicant next argues on pages 11-12 that the claimed invention should be found eligible in light of the guidance of Desjardins. Particularly, Applicant argues that the now-recited “object identification logic having an algorithm with machine learning” in combination with the camera system and signaling device provide a technical improvement. Applicant specifically argues that the inclusion of machine learning features should render the claims eligible. Examiner respectfully disagrees. Regarding the machine learning recite in the claims, Examiner notes that the object identification logic has an algorithm “with” machine learning. Accordingly, there are no recited specifics regarding how the machine learning functions as part of the object identification logic, only that some kind of machine learning at least be a part of the algorithm in some fashion. Examiner notes that this addition of general machine learning to the algorithm in the claimed invention is in contrast with the claims of Desjardins which were particularly directed towards machine learning. Instead of an improvement as in Desjardins, the claimed invention is merely attempting to tie the abstract ideas of the claimed invention to the field of machine learning. Therefore, as claimed, the analysis of MPEP 2106.05(h) applies to the claimed invention but not to Desjardins. Furthermore, the argued training of the machine learning is not recited in the claimed invention, much less particular training techniques to mitigate a technical problem like the “catastrophic forgetting” addressed in Desjardins. Applicant’s arguments that Examiner’s analysis is counter to Desjardins are not persuasive because the claimed invention is not reciting an improvement to machine learning, but tying the judicial exception of the claimed invention to the field of machine learning.
Regarding the combination of the camera system and signaling device along with the machine learning, Examiner notes that, as discussed above regarding Applicant’s Example 36 arguments, the camera system and resulting sensor data are being used as a tool and are not providing a technical improvement to object detection as argued. Similarly, the signaling device is used to output an instruction to a user loading packages to place a package in a designated spot (indicated by a light in the specification embodiments). Even when considered together these additional elements do not provide a technical improvement but rather are being applied to the judicial exception to determine a placement location for a package being loaded onto a vehicle.
Across pages 12-14, Applicant also argues that the claimed invention does not recite a certain method of organizing human activity. Applicant argues that instead of organizing human activity the claims allegedly recite a specific technical system that performs “automated technical operations”. Applicant argues that Examiner conflates the purpose of the invention with the actual limitations and features of the invention and that the features are not organizing human activity because the outputs are used by a human. Examiner respectfully disagrees.
First, Examiner notes that camera system and signaling device argued by Applicant is not a “specific technical system”. For instance, the camera system of the claims requires “a plurality of cameras” connected to a control device. The claims are silent regarding particular requirements, arrangements, or configurations that may be provide an improvement. The “sensor data” obtained by the cameras is also not recited in any particular detail, as long the data can be used to detect a person and identify a package. Regarding the signaling device, the claim does not specify what kind of signal is being output. Specification [0037] considers the signaling device could be “a first light means” including possibly a lamp, a laser, or an LED. Paragraph [0116] considers a projection of object contours or identifier. Specification paragraph [0097] implies (“The signaling device 52 is adapted to output a load space position signal, here a visual load space position signal”) that the signal output by the device may not need to be a visual signal at all. Therefore, the additional elements are not a specific technical systems as Applicant argues. Instead, they a generic components used as tools to execute an abstract idea.
Regarding the abstract idea of managing personal behavior, Applicant’s specification [0003]-[0004] recite a user loading a vehicle creating a mental map of the vehicle and personally deciding how to arrange packages in a vehicle, which Applicant states cause inefficiency based on the individual user’s experience and skill organizing the vehicle and cause a mental burden for the user needing to create such a mental map. Applicant’s claimed invention therefore, removes the mental burden and compensates for the individual skill of the user by instructing the user where to place each package. Applicant’s argument that the output of the invention is merely used by humans is incomplete because the claimed invention is directly instructing the user how best to load the packages into the vehicle. In other words, the claimed invention is managing how the user performs their job/the task of loading the vehicle. Instead of merely “providing information” to a user as Applicant argues, the claimed invention is determining based on object properties and route plan where to put an object and providing the user with an instruction as to where to put the package. Therefore, instead of the user deciding for themselves how to load the vehicle, the claimed invention is deciding for them. The claimed invention accordingly manages the user’s behavior while loading the vehicle. Regarding the MPEP and case law citations on page 13 of Remarks, Examiner notes that they are directed towards the mental process category of abstract idea, which has been discussed above.
Applicant’s arguments regarding the “closed-loop technical system” are unpersuasive for the reasoning discussed above. Namely, while the camera system and signaling device are additional elements and are treated as such in the rejections below, the claimed invention, particularly the determination of a location to place an object, the object’s dimensions, and using a removal distance to determine the location, still recites the management of personal behavior when loading a delivery vehicle. Even if the camera system, control device, and signaling device were to integrate the claims into a practical application or amount to significantly more, the claims would still recite the managing of personal behavior of the user loading the vehicle. Regarding Applicant’s additional reference to Example 45, Examiner notes that the “meaningful limitation” in the Example was the direct physical control over the molding either by opening and ejecting or temperature correction. This is not analogous to the present invention in which an instruction signal is output to tell a human where to put a package instead of automatically conveying the package to the determined location.
For the above reasons, Applicant’s arguments regarding the eligibility of the claims are not persuasive. The 35 U.S.C. 101 rejections of claims 28-34 have been maintained, and new claims 42-48 have been rejected under 35 U.S.C. 101 for similar reasoning as discussed above.
Applicant’s arguments, see pages 14-17, filed 03/12/2026, with respect to the 35 U.S.C. 103 rejections of claims 28-34 have been fully considered but are either not persuasive or moot. Claims 28-34 still stand rejected under 35 U.S.C. 103. New claims 42-48 also stand rejected under 35 U.S.C. 103.
Regarding the argued limitations of a camera system that determines presence of a user within the vehicle, using sensor data from a camera system to determine that a user is placing objects in the vehicle, and using an object identification logic with machine learning to identify objects by comparing sensor-acquired properties with route plan information, Applicant’s arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, while Schwartz teaches a system of a plurality of cameras in the vehicle connected to a controller, Schwartz does not teach the sensor data acquired from these camera being used to perform the newly amended limitations. Sangeneni et al. (U.S. Pre-Grant Publication No. 2021/0319582, hereafter known as Sangeneni) is used to teach these features as is shown below, and not any of Dholakia, Rewerts, or the other cited references of the dependent claims.
Regarding Applicant’s arguments that there would be no motivation to combine Schwartz and Dholakia to perform the camera detection of a user entering a vehicle and placing packages, Examiner respectfully disagrees. As will be discussed in more detail below, Sangeneni teaches a camera system that monitors humans loading packages into a vehicle and uses the camera system with a machine learning object recognition system to identify and scan barcodes on the items being loaded by the humans. As Schwartz also obtains information from barcode scans of packages being loaded onto a vehicle, the information obtained via the camera system of Sangeneni could be used in the downstream processing of Schwartz. One of ordinary skill in the art would have recognized that a camera system automatically scanning barcodes in the “substantially less time” (Sangeneni [0062]) would reduce the burden on the human loaders to have to repeatedly scan items themselves to receive guidance on where to load the objects in the vehicle. Therefore, there would have been motivation to combine the camera system and machine learning object recognition of Sangeneni into the system of Schwartz to reduce burden on the humans loading the packages. Therefore, Applicant’s arguments are not persuasive.
Accordingly, claims 28-34 still remain rejected under 35 U.S.C. 103. New claims 42-48 stand rejected under 35 U.S.C. 103 for similar reasoning as discussed above regarding claims 28-34.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 28-34 and 42-48 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite generating instructions on how to load/unload a delivery vehicle.
As an initial matter, claims 28-34 fall into at least the process category of statutory subject matter. Claims 42-48 fall into at least machine category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
In claim 28, the limitation of “A method, by a load space management system of a vehicle, wherein the load space management system comprises a camera system having a plurality of cameras coupled to a control device, and a signaling device coupled to the control device, the method comprising: determining, using sensor data acquired by the camera system, presence of a user within the vehicle”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a load space management system of a vehicle”, “a camera system having a plurality of cameras coupled to a control device, and a signaling device coupled to the control device”, and “sensor data,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “receiving a route plan; determining, using the sensor data, that the user is placing objects in the vehicle; determining, using the sensor data and an object identification logic having an algorithm with machine learning, identification information for each object being placed in the vehicle by comparing properties of each object acquired from the sensor data with information from the route plan; determining that a first number of objects present in the vehicle matches a second number of objects in the route plan; determining, based on the route plan and the identification information for each object, a location for placing each object within the vehicle; determining dimension information for each object; determining the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle; and outputting, by the signaling device, a load space position signal indicating the location for placing each object and orientation information for placing the object at its respective location”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 28 recites the concept of instructing a delivery driver how to load/unload packages on a delivery vehicle which is a certain method of organizing human activity including managing personal behavior. A method, the method comprising: determining, using data, presence of a user within the vehicle; receiving a route plan; determining, using the data, that the user is placing objects in the vehicle; determining, using the data and an object identification logic having an algorithm, identification information for each object being placed in the vehicle by comparing properties of each object acquired from the data with information from the route plan; determining that a first number of objects present in the vehicle matches a second number of objects in the route plan; determining, based on the route plan and the identification information for each object, a location for placing each object within the vehicle; determining dimension information for each object; determining the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle; and outputting a load space position signal indicating the location for placing each object and orientation information for placing the object at its respective location all, as a whole, fall under the category of managing personal behavior. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a load space management system of a vehicle, a camera system having a plurality of cameras coupled to a control device, a control device, a signaling device coupled to the control device, and sensor data. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional element of the object identification logic having an algorithm with “machine learning” amounts to no more than generally linking the judicial exception to the field of machine learning. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a load space management system of a vehicle, a camera system having a plurality of cameras coupled to a control device, a control device, a signaling device coupled to the control device, and sensor data amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional element of the object identification logic having an algorithm with “machine learning” amounts to no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking a judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claims 29-34 further limit the abstract idea of claim 28 without adding any new additional elements. Therefore, by the analysis of claim 28 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
In claim 42, the limitation of “determine, using sensor data acquired by the camera system, presence of a user within the vehicle”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “A delivery vehicle comprising: a load space; a load space management system comprising a control device, a communication device coupled to the control device, and a signaling device coupled to the control device; a camera system having a plurality of cameras coupled to the control device; wherein the control device is configured to” and “sensor data,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “receive a route plan; determine, using the sensor data, that the user is placing objects in the vehicle; determine, using the sensor data, and an object identification logic having an algorithm with machine learning, identification information for each object being placed in the vehicle by comparing properties of each object acquired from the sensor data with information from the route plan; determine that a first number of objects present in the vehicle matches a second number of objects in the route plan; determine, based on the route plan and the identification information for each object, a location for placing each object within the vehicle; determine dimension information for each object; determine the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle; and output, using the signaling device, a load space position signal indicating the location for placing each object and orientation information for placing the object at its respective location”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 28 recites the concept of instructing a delivery driver how to load/unload packages on a delivery vehicle which is a certain method of organizing human activity including managing personal behavior. Determine, using data, presence of a user within the vehicle; receive a route plan; determine, using the data, that the user is placing objects in the vehicle; determine, using the data, and an object identification logic having an algorithm, identification information for each object being placed in the vehicle by comparing properties of each object acquired from the data with information from the route plan; determine that a first number of objects present in the vehicle matches a second number of objects in the route plan; determine, based on the route plan and the identification information for each object, a location for placing each object within the vehicle; determine dimension information for each object; determine the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle; and output, a load space position signal indicating the location for placing each object and orientation information for placing the object at its respective location all, as a whole, fall under the category of managing personal behavior. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a delivery vehicle comprising a load space and a load space management system, a control device, a communication device coupled to the control device, a signaling device coupled to the control device, a camera system having a plurality of cameras coupled to a control device, and sensor data. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional element of the object identification logic having an algorithm with “machine learning” amounts to no more than generally linking the judicial exception to the field of machine learning. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a delivery vehicle comprising a load space and a load space management system, a control device, a communication device coupled to the control device, a signaling device coupled to the control device, a camera system having a plurality of cameras coupled to a control device, and sensor data amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional element of the object identification logic having an algorithm with “machine learning” amounts to no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking a judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claims 43-48 further limit the abstract idea of claim 42 without adding any new additional elements. Therefore, by the analysis of claim 42 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 28-30, 32, 42-44, and 46 are rejected under 35 U.S.C. 103 as being unpatentable over Schwartz et al. (U.S. Pre-Grant Publication No. 2019/0152376, hereafter known as Schwartz) in view of Sangeneni et al. (U.S. Pre-Grant Publication No. 2021/0319582, hereafter known as Sangeneni), Dholakia et al. (U.S. Patent No. 10,867,275; hereafter known as Dholakia), and Toebes et al. (U.S. Pre-Grant Publication No. 2020/0055671, hereafter known as Toebes).
Regarding claim 28, Schwartz teaches:
A method, by a load space management system of a vehicle, wherein the load space management system comprises a camera system having a plurality of cameras coupled to a control device, and a signaling device coupled to the control device, the method comprising: (see Fig. 3 and [0068]-[0095] for overall method of managing how items are loaded onto a delivery vehicle. See Fig. 2 and [0055]-[0067] for the load space management system of a vehicle including on-vehicle controller 240 discussed in [0062]. For a camera system of a plurality of cameras coupled to a control device see sensors 223 coupled to on-vehicle controller 240 and controller 210 in Fig. 2 as well as [0046] “The plurality of shelves 120a-d can also include one or more sensors 123 attached to a surface of one or more of the shelves 120a-d or attached proximate one or more of the plurality shelves 120a-d. The sensors 123 can be…cameras, and the like”. For a signaling device coupled to the control device, see indicators 222 connected to on-vehicle controller and controller in Fig. 2 as well as [0048]-[0054] for shelf and floor indicators using LEDs to indicate shelf positions for packages)
determining, using sensor data (See [0090] “the carrier can place the item on the proposed shelf location, and scan the item on the shelf, or can scan a code on the shelf where the item was placed. This can signal the loading process for the item is complete. In some embodiments, the carrier can scan the next item to be loaded. The scanning of the next item to be loaded can indicate to the on-vehicle controller 240 that the carrier has placed the item on the proposed shelf location” for the load space management vehicle system receiving an indication that the carrier has entered a vehicle and placed a package within the vehicle via scan data)
receiving a route plan (see step 306 and [0072] "Once the delivery point is identified in block 304, the process 300 moves to block 306, wherein the route and item information for the item is identified. When the delivery point is identified, the controller 210 can look up the route and item information in the item database 230" and [0073] for receiving the route plan for a vehicle from a database. Also see [0075] for route stop information forwarded to the on-vehicle controller)
determining, using the sensor data, that the user is placing objects in the vehicle (see [0090] “the carrier can place the item on the proposed shelf location, and scan the item on the shelf, or can scan a code on the shelf where the item was placed. This can signal the loading process for the item is complete. In some embodiments, the carrier can scan the next item to be loaded. The scanning of the next item to be loaded can indicate to the on-vehicle controller 240 that the carrier has placed the item on the proposed shelf location” and [0046] “The sensors 123 can be photoelectric sensors, ultrasonic sensors, weight sensors, cameras, and the like. The one or more sensors 123 are configured to identify when an item, such as a package, is placed on or removed from one of the plurality of shelves 120a-d” for the load space management vehicle system receiving an indication that the carrier has placed an item within a vehicle based on the camera sensors and scanning of items and is scanning additional items to be loaded into the vehicle. See [0068]-[0069] for scanning the item to be loaded onto a vehicle)
determining, using the sensor data (see [0070] “The process 300 next moves to block 304, wherein the item delivery point is identified. In some embodiments, the item delivery point can be identified by using a scanned item identifier and looking up the associated information in the item database 230. The item database, as described above, stores information about the item and about the delivery point for the item” and [0072] “Once the delivery point is identified in block 304, the process 300 moves to block 306, wherein the route and item information for the item is identified. When the delivery point is identified, the controller 210 can look up the route and item information in the item database 230” for determining information regarding an item to be loaded into a vehicle by comparing a delivery destination from a scanned barcode for an item with a route plan database. See [0059] “Item information can include one or more of the following: item size, weight, description, sender, recipient, intended destination/delivery point, origination point, return address, a tracking number, a unique identifier, class of service, postage status, and the like” for the information being identification information of the item including delivery destination)
determining that a first number of objects present in the vehicle matches a second number of objects in the route plan (see [0092] “The process next moves to decision state 316, wherein it is determined whether all items have been loaded. The controller 210 can receive signals from the mobile computing device 210 and/or the on-vehicle controller 240 indicating that the loading of an item is completed, and storing the shelf location of the item. The controller 210 can receive this information and determine whether each of the items intended to be loaded on the vehicle 100 have been loaded. This determination can also be done at the on-vehicle controller 240 or in the mobile computing device 220, or by a combination of any of these components” for determining whether a number of items that have been loaded onto the vehicle match the items that are intended to be loaded onto the vehicle. See [0094] “If it is determined that all of the items intended for loading on the vehicle 100 have been loaded, the process 300 moves to block 318 and ends” for determining that the number of items that have been loaded onto the vehicle matches the number of items intended to be loaded onto the vehicle)
determining, based on the route plan and the identification information for each object, a location for placing each object within the vehicle (see [0075] "The process 300 then moves to block 308, wherein a proposed shelf location for the item is identified...The on-vehicle controller 240 determines the proposed shelf location using information regarding the delivery route, anticipated item volume for the delivery route, the delivery points and their stop numbers, item size, or any other desired factors. For example, where the scanned item is intended for a destination at the beginning of the route, the on-vehicle controller 240 may determine the proposed shelf location at a point on a shelf 120a-d or a floor storage area 112a-b near the back of the cargo portion 102, nearer the door 104 than the driver portion 103. If the item is indented for a delivery point which occurs later in the route, such as in the last half or last quarter, the on-vehicle controller 240 determines a potential item storage location which is nearer the front of the cargo portion, or nearer the driver portion" for determining a load space location for an item to be introduced into the load space of the vehicle taking into consideration the stop on the route plan the item is to be delivered at and the size of the item itself. See [0082] for load plan determined in advance of the items being introduced to the space and [0076]-[0081] for other considerations when determining a load space position)
determining dimension information for each object (see [0078] “in addition to receiving the item delivery point, the on-vehicle controller receives item information from the controller 210 or be mobile computing device 220. The item information can include item dimensions, weight, and/or special handling instructions. The on-vehicle controller 240 can include the item dimensions to determine the proposed shelf location” for determining item dimensions based on item information. See [0090] for scanning multiple items into the vehicle)
outputting, by the signaling device, a load space position signal indicating the location for placing each object (see [0083] "After a proposed shelf location is determined, the process moves to block 310, wherein the indicators 122, 114 indicate the proposed shelf location. In one example, the indicators 122, 114 are lights that illuminate a shelf location 145, similar to that depicted in FIG. 1C" and see [0084] for alternate forms of indicator devices that can also be used to show the driver/loader where to place the identified item)
While Schwartz teaches a camera system of a plurality of cameras connected to a controller, Schwartz does not explicitly teach using sensor data acquired by the camera system to determine the presence of a user within the vehicle and determining identification information for each object in conjunction with object identification logic having an algorithm with machine learning. As discussed above, while Schwartz teaches indicating where to place the item to be loaded into the delivery vehicle, Schwartz does not explicitly teach the indication including the orientation of how the carrier is supposed to place the item at the location. Schwartz also does not explicitly teach determining the placement location based on a removal distance information indicative of a distance between two immediately adjacent objects when placed inside the vehicle. Sangeneni teaches:
the load space management system comprises a camera system having a plurality of cameras coupled to a control device (see [0027] “the cameras (208) include one or more RGB or stereo vision IR depth cameras. In an embodiment, cameras (208) are configured in and around the cargo space and provide a full coverage of the cargo space. The cargo space comprises a contained space that may or may not include shelves or other organizational elements where the cameras (208) are configured such that the field of view covers the full volume under and around the organizational elements or lack thereof” for cameras being positioned in and around a vehicle cargo space. See Fig.2 and [0024]-[0025] for the cameras being connected to the system processor)
determining, using sensor data acquired by the camera system, presence of a user within the vehicle (see [0031] “Referring again to FIG. 3A block 304, the disclosed techniques of vehicular cargo management include detecting a package drop/load, upon detection of a person entering the vehicle cargo space. In an embodiment, a camera may be mounted above the van's door, pointing to the floor and covering an area that includes several feet on the interior and exterior sides of the van door.” See [0033]-[0037] for the detection of a human entering the vehicle)
determining, using the sensor data, that the user is placing objects in the vehicle (see [0031] “Referring again to FIG. 3A block 304, the disclosed techniques of vehicular cargo management include detecting a package drop/load, upon detection of a person entering the vehicle cargo space…Activity is detected using depth thresholding as described below, and the system (104) may determine the type of object being loaded (a parcel or another object such as a human) by using a computer vision deep neural network (DNN) to differentiate between the parcel and other objects” and [0036]-[0038] for detecting parcels are being loaded)
determining, using the sensor data and an object identification logic having an algorithm with machine learning, identification information for each object being placed in the vehicle (see [0045] “the camera (208) may use a barcode on the parcels to identify a specific parcel inside the cargo space. As is known in the art, barcodes applied on the parcels can be decoded to retrieve various information regarding the parcels, such as the origin of the parcel, a destination location for the parcel and the like” and [0046] “the camera (208) may capture multiple frames of the event and send them to a DNN model to train the model in accurately identifying the barcode images. This DNN assists in further improving the quality of captured barcode images by providing assistance in identifying barcodes in the image” and [0031] “the system (104) may determine the type of object being loaded (a parcel or another object such as a human) by using a computer vision deep neural network (DNN) to differentiate between the parcel and other objects” for camera data and object identification logic having a computer vision deep neural network to distinguish between humans and the parcel barcodes to decode barcodes and obtain information about the package. See [0048]-[0066] for additional detail regarding the use of the DNN to extract barcode information, particularly [0059] for identifying a package and comparing the package properties to a manifest for the vehicle. In combination with Schwartz, cameras installed in and around the vehicle cargo area perform the barcode scanning instead of the human loading the package)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the vehicle loading management camera system of Sangeneni to identify users and the parcels they are loading into the vehicle into the system of Schwartz. As Schwartz already teaches a plurality of cameras in the vehicle, one of ordinary skill in the art would have recognized that the addition of additional cameras would have predictable results. Furthermore, while Schwartz teach the human loading the vehicle as the one scanning the package barcodes, the camera and DNN systems of Sangeneni “directly describe the overall scanning pipeline and logic of how a barcode is detected and decoded in substantially less time” (paragraph [0062]). Therefore, by incorporating the camera and DNN system of Sangeneni into Schwartz, the barcode scanning of packages being loaded can be performed in less time. Furthermore, one of ordinary skill in the art also would have recognized an advantage for the loading user, as the user would no longer need to scan the barcodes of packages themselves to receive the placement guidance from Schwartz. The combined system would automatically scan the barcodes and provide the appropriate placement indication without the additional effort from the user.
The combination of Schwartz and Sangeneni still does not explicitly teach the indication including the orientation of how the carrier is supposed to place the item at the location. The combination of Schwartz and Sangeneni also does not explicitly teach determining the placement location based on a removal distance information indicative of a distance between two immediately adjacent objects when placed inside the vehicle.
Dholakia teaches indicating a location at which to put a delivery item and the orientation in which to place the item (see Col. 9 lines 53-62 "the agent 103 can be notified of the optimal placement location for the package 106 via the identifying device 112. For example, the identifying device 112 can project a light highlighting the area where the package 106 is to be placed. In some embodiments, the agent 103 can be notified of the optimal placement location for the package 106 via an audio signal broadcasted via the audio device 242 (FIG. 2). In some embodiments, the agent 103 can be notified of the orientation of the package 106 for the particular empty space 115". See Col. 10 lines 35- 53 for the package loading system determining the best orientation of the package to fit in an empty space).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the orientation of an object into a load space position for an object and indicating the intended orientation for the object as part of the position signal of Dholakia into the combination of Schwartz and Sangeneni. As Dholakia states in Col. 2 lines 24-29 “the current loading configuration and the package data can be used as inputs to an optimization engine that is configured to determine the optimal placement and orientation for incoming packages such that the amount of empty space within the delivery vehicle is minimized and the cubic efficiency is improved”. Therefore, by considering the orientation of the packages being loaded onto the vehicle and communicating the intended orientation to the user via visual and audio means, the resulting combination would make more efficient use of the space of the vehicle.
The combination of Schwartz, Sangeneni, and Dholakia still does not explicitly teach determining the placement location based on a removal distance information indicative of a distance between two immediately adjacent objects when placed inside the vehicle. Toebes teaches:
determining the location for placing each object based at least in part on a removal distance to be provided between two immediately adjacent objects when placed inside the vehicle (see [0022] “The storage racks 30 are each configured (e.g., include suitable shelving, etc.) so that the storage containers 40 are placed on the shelf 36 in a tightly packed storage density—where tightly packed storage density refers to placement of containers 40 adjacent one another so that the lateral sides 40L1, 40L2 of the adjacent containers 40 have a minimal clearance between them or are substantially touching one another but can be inserted or removed from the shelf 36 without disturbing a shelf position of adjacent containers” and [0028] “the container 40 can be placed on a shelf 36 in a known/predetermined location (e.g., to place the containers 40 on the shelf 36 in a tightly packed storage density—where tightly packed storage density refers to placement of containers 40 adjacent one another so that the lateral sides 40L1, 40L2 of the adjacent containers 40 have a minimal clearance between them or are substantially touching one another but can be inserted or removed from the shelf 36 without disturbing a shelf position of adjacent containers)” for the predetermined location placement of containers such that the containers are tightly packed but still leave minimal clearance so they can be removed without disturbing neighboring containers. In combination with Schwartz, the predetermined location indicated to the loading user is based on such a removal distance between items)
One of ordinary skill in the art would have recognized that applying the known technique of placing items on shelving based on a minimum clearance to allow for subsequent removal from the shelving of Toebes to the combination of Schwartz, Sangeneni, and Dholakia would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Toebes to the teaching of the combination of Schwartz, Sangeneni, and Dholakia would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such placing items on shelving based on a minimum clearance to allow for subsequent removal from the shelving. Further, applying placing items on shelving based on a minimum clearance to allow for subsequent removal from the shelving to the combination of Schwartz, Sangeneni, and Dholakia would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient retrieval of the items when it is time for unloading. As Toebes states in [0022] and [0028], by placing the items in such a manner, neighboring items will not be disturbed when an item is taken from the shelf. One of ordinary skill in the art would have recognized that allowing for such a removal distance would accordingly prevent other packages from falling/breaking/spilling when the vehicle arrives at a delivery destination and an item(s) are removed from the vehicle for delivery.
Regarding claim 29, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 28 above. While Schwartz considers the sequence of delivery destinations for the items when determining where to place the items (see at least [0077]), the combination of Schwartz and Sangeneni does not explicitly teach determining a sequence of placing the items into the vehicle based on the items’ identification information. Dholakia further teaches:
determining, based on the identification information, a sequence for placing the objects in the vehicle (see Col. 3 lines 24-32 “the package loading system 100 can determine a package sequence for loading based on the incoming packages 106. As such, the next arriving package 106 being transported via the conveyor system 121 may not correspond to the first package 106 in the package sequence. In such instances, the agent 103 can place the next arriving package 106 in a secondary location (e.g., off to the side, in a container, etc.) until notified to load the particular package 106” and Col. 9 lines 5-38 for calculating the placement and package load sequence to maximize cubic efficiency of the vehicle load space)
It would have been obvious before the effective filing date of the claimed invention to incorporate the package load sequencing of Dholakia into the combination of Schwartz, Sangeneni, Dholakia, and Toebes. As Dholakia states in Col. 9 lines 6-9 “The package sequence corresponds to a sequence in which the packages can be loaded to maximize cubic efficiency within the vehicle 109”. Accordingly, by instructing the carrier to load packages into the vehicle in a particular order, one of ordinary skill in the art would have recognized that the most efficient usage of the load space of Schwartz could be realized regardless of the order in which the packages arrive at the vehicle.
Regarding claim 30, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 28 above. As discussed above regarding claim 28, the combination of Schwartz and Sangeneni does not explicitly teach the consideration of package orientation when indicating where to place the item being loaded on the vehicle. Accordingly, the combination of Schwartz and Sangeneni does not explicitly teach determining that an object has been placed in an incorrect orientation and outputting a message indicating the incorrect placement of the object. However, Dholakia further teaches:
determining that at least one object is placed in an incorrect orientation; and outputting a message indicating the incorrect placement of the object (see Col. 13 lines 16-37 “At box 718, the package loading system 100 verifies that the package 106 has been placed in the optimal package placement location… the optimization engine 100 can compare a loading configuration 403 obtained prior to the placement of the package 106 with a loading configuration 403 corresponding to the obtained point cloud data following the placement to determine whether the package was placed in the correct location. In some embodiments, the package loading system 100 can notify the agent 103 of an improper placement and/or package 106 if detected. For example, the package loading system 100 can notify the agent 103 of an improper placement and/or package 106 via a user interface 221 rendered on the display 209 of the computing device 114. In another example, the package loading system 100 can notify the agent 103 of an improper placement via an auditory signal via the audio device 242”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate alerting the carrier of an improperly placed package and indicating the intended orientation for the object of Dholakia into the combination of Schwartz, Sangeneni, Dholakia, and Toebes. As Dholakia states in Col. 2 lines 24-29 “the current loading configuration and the package data can be used as inputs to an optimization engine that is configured to determine the optimal placement and orientation for incoming packages such that the amount of empty space within the delivery vehicle is minimized and the cubic efficiency is improved”. Therefore, by considering the orientation of the packages being loaded onto the vehicle and communicating the intended orientation to the user via visual and audio means, the resulting combination would make more efficient use of the space of the vehicle.
Regarding claim 32, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 28 above. Schwartz further teaches:
wherein the identification information includes a delivery address for each object (see [0078] “The item information can include item dimensions, weight, and/or special handling instructions” and [0059] “Item information can include one or more of the following: item size, weigh…intended destination/delivery point”)
While Schwartz considers the sequence of delivery destinations for the items when determining where to place the items (see at least [0077]), the combination of Schwartz and Sangeneni does not explicitly teach determining a sequence of placing the items into the vehicle based on the items’ identification information. Dholakia further teaches:
determining, based on the identification information and the dimension information, a sequence for loading each of the objects into the vehicle (see Col. 3 lines 24-32 “the package loading system 100 can determine a package sequence for loading based on the incoming packages 106. As such, the next arriving package 106 being transported via the conveyor system 121 may not correspond to the first package 106 in the package sequence. In such instances, the agent 103 can place the next arriving package 106 in a secondary location (e.g., off to the side, in a container, etc.) until notified to load the particular package 106” and Col. 9 lines 5-38 for calculating the placement and package load sequence to maximize cubic efficiency of the vehicle load space. See Col. 9 lines 33-37 “A package wall can be built in layers of progressive mass and volume. For example, large and heavy packages 106 (e.g., exceeding a weight and/or dimension threshold) that are tagged by the optimization engine 245 will form the base of each wall” for the sequence also being based on package dimension)
It would have been obvious before the effective filing date of the claimed invention to incorporate the package load sequencing of Dholakia into the combination of Schwartz and Sangeneni. As Dholakia states in Col. 9 lines 6-9 “The package sequence corresponds to a sequence in which the packages can be loaded to maximize cubic efficiency within the vehicle 109”. Accordingly, by instructing the carrier to load packages into the vehicle in a particular order, one of ordinary skill in the art would have recognized that the most efficient usage of the load space of Schwartz could be realized regardless of the order in which the packages arrive at the vehicle.
Regarding claim 42, Schwartz teaches:
A delivery vehicle comprising: a load space (see Fig. 1A and [0044]-[0045] for a vehicle comprising a cargo portion)
a load space management system comprising a control device (see Fig. 2 and on-vehicle controller 240 and [0062] “The on-vehicle controller 240 can be similar to the controller 210 described herein. The on-vehicle controller 240 is located on a delivery vehicle, and can be integrated into the vehicle's existing computer systems or it can be an additional system added to the vehicle.”)
a communication device coupled to the control device (see [0057] for the communication feature as part of the controller. See [0062] for the on-vehicle controller being similar to the controller 210 and likewise having a communication feature)
and a signaling device coupled to the control device (see indicators 222 connected to on-vehicle controller and controller in Fig. 2 as well as [0048]-[0054] for shelf and floor indicators using LEDs to indicate shelf positions for packages)
a camera system having a plurality of cameras coupled to the control device; wherein the control device is configured to (see sensors 223 coupled to on-vehicle controller 240 and controller 210 in Fig. 2 as well as [0046] “The plurality of shelves 120a-d can also include one or more sensors 123 attached to a surface of one or more of the shelves 120a-d or attached proximate one or more of the plurality shelves 120a-d. The sensors 123 can be…cameras, and the like”)
Regarding the remaining limitations of claim 42, see the rejection of claim 28 above.
Regarding claim 43, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 42 above. Regarding the limitations introduced in claim 43, see the rejection of claim 29 above.
Regarding claim 44, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 42 above. Regarding the limitations introduced in claim 44, see the rejection of claim 30 above.
Regarding claim 46, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 42 above. Regarding the limitations introduced in claim 46, see the rejection of claim 32 above.
Claims 31 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Schwartz in view of Sangeneni, Dholakia, Toebes, Soles et al. (U.S. Patent No. 11,514,386; hereafter known as Soles), and Gil (U.S. Pre-Grant Publication No. 2019/0143872, hereafter known as Gil).
Regarding claim 31, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 28 above. Schwartz further teaches:
determining, based on the identification information, a first object and a second object having a (see [0070] “The process 300 next moves to block 304, wherein the item delivery point is identified. In some embodiments, the item delivery point can be identified by using a scanned item identifier and looking up the associated information in the item database 230. The item database, as described above, stores information about the item and about the delivery point for the item” for determining a delivery destination for an item. See [0090] for scanning multiple items)
While Schwartz teaches identifying the delivery address of each package and Dholakia teaches packages being stacked on top of each other, the combination of Schwartz, Sangeneni, Dholakia, and Toebes does not explicitly teach determining two packages have the same destination, and instructing the two packages to be stacked on top of each other based on the delivery destination and the package dimensions. Soles teaches:
and outputting, based on of the first object and the second object, instructions to stack the first object on top of the second object (see Col. 12 lines 33-37 for a packing plan to load items into a vehicle. See Col. 12 lines 43-57 "planning engine 202 may generate a packing plan based on constraints associated with the packing area, the items, the packages, and/or the containers...When generating the packing plan, planning engine 202 may identify one or more constraints that limit the location of the products on the pallets and the pallets in the truck based on, for example, the size, weight, and fragility of the products and weight and height restrictions for the truck and pallets" for the packing plan being determined based on the constraints of the load space/area on the vehicle and the constraints of the products being loaded onto the vehicle. See Col. 4 lines 48-58 "Instruction rendering device 120 may display visual information, such as, for example, instructions, feedback, or other information by superimposing one or more visual elements over a packing area or an object, such as, an item, package, or container. For example, visual elements (such as, for example, graphics, colors, and text) may display instructions, feedback, or other information and may be displayed so that the visual elements appear superimposed on real-world packing areas and objects such as, for example, shelves, boxes, pallets, shipping containers, trucks, trailers, warehouse locations, and the like" and Col. 5 lines 4-17 "by superimposing visual elements over real-world objects, one or more instruction rendering devices 120 generates a visualization that identifies the location and size of the packing area, displays the instructions for packing objects in a packing area directly on the objects and packing area...displays data describing detected objects in connection with the detected objects including, for example...packing instructions (such as whether a particular side needs to be in an up direction, how many other objects may be packed on top of the object, how the object is to be oriented, where in a package, container, or packing area the object is to be placed" for the load space position signal projecting text packaging instructions indicating the number of packages to be stacked together)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the load space position indicating a stacking of multiple objects based on the delivery vehicle and object properties and further indicating the stacking to the user via the load position signal of Soles into the combination of Schwartz, Sangeneni, Dholakia, and Toebes. As Soles states in Col. 1 lines 32-45 “Shipping items between various supply chain entities may require selecting various items, containers, or packages and packing these objects in a particular configuration to avoid damage during shipment. Because any particular objects may comprise various different sizes, determining a configuration that prevents damage while ensuring efficient use of space is difficult. Even when the selection of objects is known beforehand, a suitable configuration indicating how to pack and stack one or more containers, packages, or items, is not known. As a result, objects packed on a pallet, for example, are frequently improperly packed and stacked, which reduces, among other things, the shipment's strength, torsion, and resistance to airflow. The inability to quickly generate proper packing configurations is undesirable”. Therefore, by incorporating the determination of package stacking based on the delivery vehicle storage area and the weight/fragility of the packages would allow the user to make the most efficient use of the delivery vehicle space without risking damage to the vehicle or the packages being loaded onto the vehicle.
The combination of Schwartz, Sangeneni, Dholakia, Toebes, and Soles still does not explicitly teach determining that multiple packages have the same delivery destination and outputting instructions to stack the objects based on the objects sharing a delivery destination. However, Gil teaches:
determining, based on the identification information, a first object and a second object having a same delivery address (see [0182], [0189] for packages being determined to have the same delivery destination)
and outputting, based on the delivery address and dimension information of the first object and the second object, instructions to stack the first object on top of the second object (see [0027] “the picking robot may be configured to place packages destined for delivery at an upcoming destination location in a delivery staging area (which may be within a cargo area of the vehicle). Multiple packages destined for delivery at a single destination may be stacked in the delivery staging area” for the packages being stacked because they are bound for the same delivery destination. In combination with Soles, the stacks would be made of packages going to the same delivery destination and based on the dimensions/stackability of the packages)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the stacking of packages based on the packages being determined to share the same delivery destination of Gil into the stacking determination of the combination of Schwartz, Sangeneni, Dholakia, Toebes, and Soles. As Gil states in [0027] “Multiple packages destined for delivery at a single destination may be stacked in the delivery staging area (e.g., on a delivery cart) to ease the delivery vehicle operator's task for retrieving multiple packages for delivery at a particular destination location.” One of ordinary skill in the art would have recognized that by also taking into consideration delivery destinations when stacking packages, and further stacking packages that shared a common destination, the final delivery process for the vehicle driver can be streamlined. The driver would only have to go to one location in the truck and retrieve a stack of multiple packages as opposed to being instructed to potentially take packages from multiple locations in the cargo area of the vehicle. Thus, by stacking packages according to destination, the final delivery stops at destinations would be made more efficiently.
Regarding claim 45, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 42 above. Regarding the limitations introduced in claim 45, see the rejection of claim 31 above.
Claims 33 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Schwartz in view of Sangeneni, Dholakia, Toebes, and Aladesuyi (WIPO Publication No. 2011/038018, hereafter known as Aladesuyi).
Regarding claim 33, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 28 above. Schwartz teaches in [0092] that the system checks whether the loading process is complete after the placing of each object and [0094] checking whether all packages intended for loading have actually been loaded onto the vehicle. Sangeneni further teaches an alert of a user loading a package onto an incorrect vehicle in [0060]. However, the combination of Schwartz, Sangeneni, Dholakia, and Toebes does not explicitly teach receiving an indication that the loading process is complete for all objects, determining that the number of packages loaded on the vehicle does not match the number of packages in the route plan, and outputting a message indicating the mismatch between the number of packages loaded and the number of packages on the route plan. Aladesuyi teaches:
receiving an indication that a loading process is complete for all the objects (see [0034] “when truck 7 is loaded and cargo door 17 is closed, door sensor 18 alerts computer system 4 that cargo door 17 is closed, and computer system 4 reviews the manifest to ensure that all packages 13 have been identified as loaded… Upon receipt of the cargo door closed notification, GPS/RFID unit 19 can transmit all data to server 2 or computer system 4 to confirm that the packages loaded into cargo area 16 correspond to the packages in the manifest. Further, as cargo door 17 is closed, no packages will be entering or leaving cargo area 16” for receiving a cargo door closed notification as an indication that the loading process in completed)
determining a third number of objects loaded into the vehicle; determining that the third number of objects is different from the second number of objects; and outputting a message indicating a mismatch between the third number of objects and the second number of objects (see [0034 “GPS/RFID unit 19 can be programmed to send GPS location data when door sensor 18 indicates cargo door 17 is open and to read and store the RFIDs of the identification tags passing through the scanning area until a signal is received from door sensor 18 that cargo door 17 is closed…Upon receipt of the cargo door closed notification, GPS/RFID unit 19 can transmit all data to server 2 or computer system 4 to confirm that the packages loaded into cargo area 16 correspond to the packages in the manifest….If all packages are accounted for, no action is taken, but if packages on the manifest are not identified as loaded, the driver is alerted, e.g., via communication over device 9 of the packages not yet accounted for. Further, server 2 or computer system 4 can alert the warehouse personnel to find the specific missing packages in the manner discussed above. Once the packages are found, cargo door 17 is opened, which reactivates GPS/RFID unit 19 and RFID reader 20 to scan the new packages entering cargo area 16 to confirm that the correct packages corresponding to the manifest have been loaded into cargo area 16” for determining a number of objects loaded onto the vehicle before the door is closed, determining that the first number of objects is less than the second number of objects listed on the manifest because of missing packages from the manifest, and outputting an alert to driver/warehouse personnel to find the missing packages. Examiner notes that the number of packages loaded onto the vehicle must be less than the manifest because extraneous packages not on the manifest are removed during the loading process, see [0034] “When a package 13 is loaded that is not on the driver's manifest, server 2 or computer system 4 can identify the error through comparison with the manifest entries and send an audio alert, e.g., through a speaker 24 mounted in cargo area 16, or a text alert to device 9 informing the driver to remove the extra package from truck 7”)
One of ordinary skill in the art would have recognized that applying the known technique of determining packages from the manifest are missing from the truck upon the completion of the loading process and alerting personnel which packages are missing of Aladesuyi to the combination of Schwartz, Sangeneni, Dholakia, and Toebes would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Aladesuyi to the teaching of the combination of Schwartz, Sangeneni, Dholakia, and Toebes would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such determining packages from the manifest are missing from the truck upon the completion of the loading process and alerting personnel which packages are missing. Further, applying determining packages from the manifest are missing from the truck upon the completion of the loading process and alerting personnel which packages are missing to the combination of Schwartz, Sangeneni, Dholakia, and Toebes would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more accurate loading process for vehicles. Particularly, one of ordinary skill in the art would have recognized that the alert indicating the specific missing packages would prevent drivers from leaving without loading all of the necessary packages as well as help the drivers/warehouse personnel locate the specific missing packages and remedy the oversight more quickly than in the combination of Schwartz and Dholakia alone.
Regarding claim 47, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 42 above. Regarding the limitations introduced in claim 47, see the rejection of claim 33 above.
Claims 34 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over Schwartz in view of Sangeneni, Dholakia, Toebes, and Brown et al. (U.S. Pre-Grant Publication No. 2022/0306165, hereafter known as Brown. Examiner notes that Brown’s publication date falls outside of the 1 year grace period for the instant application).
Regarding claim 34, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 28 above. Schwartz further teaches in [0083] "the process moves to block 310, wherein the indicators 122, 114 indicate the proposed shelf location. In one example, the indicators 122, 114 are lights that illuminate a shelf location 145, similar to that depicted in FIG. 1C. As described, the proposed shelf location can appropriate a certain length of a shelf 120a-d. The on-vehicle controller 240 will instruct the indicators 122 to illuminate the portion of the shelf appropriated for the proposed shelf location". However, the combination of Schwartz, Sangeneni, Dholakia, and Toebes does not explicitly teach that the signaling device outputs a first signal to indicate a region of the vehicle where the object is to be placed and output a second signal indicating a specific location within the region where the object is to be placed. Brown teaches:
wherein providing information about the location further comprises: causing a signaling device of the vehicle to output a first signal, the first signal indicating a region of the vehicle where each object is to be placed; (see [0039] "the package delivery apparatus 170 may activate a set of lights to produce a light sequence that indicates a direction of travel for the customer 105 and/or a set of beepers to produce a sound sequence that indicates a direction of travel for the customer 105...the package delivery apparatus 170 may evaluate an image provided by the camera 110 and determine that the customer 105 is located near the front end of the autonomous vehicle 100. Accordingly, the package delivery apparatus 170 may activate a light 225 followed by a light 230 and/or produce a sound through a beeper 240 followed by a sound through a beeper 245, in order to guide the customer 105 to the compartment 135 (for either loading or for retrieving a package)" for a sequence of lights guiding a user to the proper location to load a package. Specifically, the lights indicate that the region the package is to be placed is further back in the truck than the user is currently located. In combination with Schwartz, the LED lights on the shelf activate in a sequence to guide the loader to the proper place on the shelf)
and causing the signaling device to output a second signal, the second signal indicating a specific location within the region where each object is to be placed (see [0037] “the package delivery apparatus 170 may determine a proximity of the customer 105 to the right side of the autonomous vehicle 100 and activate a light 215 that indicates the location of the compartment 135…The light 215, which can be mounted at any of various locations such as, for example, adjacent to the compartment 135 or in a handle of a door of the compartment 135, may be activated to produce a steady light or a flashing sequence” for activating a second signal to indicate the specific location in which the package is to be placed)
One of ordinary skill in the art would have recognized that applying the known technique of activating lights in sequence to guide a user to a location in which to store a package of Brown to the combination of Schwartz, Sangeneni, Dholakia, and Toebes would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Brown to the teaching of the combination of Schwartz, Sangeneni, Dholakia, and Toebes would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such activating lights in sequence to guide a user to a location in which to store a package. Further, applying activating lights in sequence to guide a user to a location in which to store a package to the combination of Schwartz, Sangeneni, Dholakia, and Toebes would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient guidance to the user about where to place the package. As Schwartz states in [0075], the specified storage location for a package could be at the back of the cargo portion towards the driver door. By incorporating the sequence of lights of Brown, which Brown states guide a user from one side of a vehicle to another in [0039] above, the resulting combination would more clearly guide the user loading packages to those determined locations that are on the far side of the vehicle from where the user currently is.
Regarding claim 48, the combination of Schwartz, Sangeneni, Dholakia, and Toebes teaches all of the limitations of claim 42 above. Regarding the limitations introduced in claim 48, see the rejection of claim 34 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kisiler et al. (U.S. Pre-Grant Publication No. 2018/0018619) teaches identifying items to be loaded into a vehicle and providing instructions regarding the arrangement of items in the vehicle
Choi et al. (U.S. Pre-Grant Publication No. 2024/0005263) teaches camera systems monitoring the activity of human loaders loading items into a cargo bay of a vehicle
Grob et al. (U.S. Pre-Grant Publication No. 2023/0214951) teaches optimizing the placement of objects in the trunk of a car based on the car’s make and model
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/M.C.M./Examiner, Art Unit 3628
/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
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